When to use which LLM
Choosing the right tool for the task is the single biggest lever on AI energy use. Reserve heavy reasoning models for high-value work, default to efficient tiers for everyday queries, and skip AI entirely where simpler tools do the job.
Drafting emails & meeting notes
High productivity gain, tiny model is sufficient. Tier-A models only.
Summarising documents
Saves hours of human attention; energy per summary is negligible vs. the time saved.
Code autocomplete & refactors
Use a small model for completion; reserve large models for architecture questions.
Customer support triage
Route first-line responses through a Tier-A model with human escalation.
Generating images for every blog post
Image generation is ~100× more energy than text. Use only when the image earns its place.
Real-time AI video for internal demos
Highest energy class. Record once or use static assets instead.
Running 'deep research' on routine questions
Reasoning loops can use 10–30× a standard query. Save for genuinely complex problems.
AI search instead of a known URL
If you know the source, go direct. AI search is convenient but wasteful as a habit.
Multi-agent workflows
Powerful, but each step compounds energy. Cap step count and prefer smaller agents.
Fine-tuning on your data
Training is energy-intensive up-front but pays back if it lets you use a smaller model.
Voice transcription & translation
Mature, efficient, high human value.
AI-generated slide decks end-to-end
Worth it for first drafts; don't regenerate 20 versions.
Rule of thumb
- Tier A (Gemini Flash, Claude Haiku, GPT-4o mini): default for 70%+ of prompts — summaries, drafts, classification, extraction.
- Tier B–C (Gemini Pro, Claude Sonnet, GPT-4o): reach for these on nuanced writing, code review, or multi-step reasoning.
- Tier D (Claude Opus, GPT-5 / o-series): reserve for genuinely hard reasoning — research, complex code, strategy. A single deep-reasoning run can use 10–30× a standard query.